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- All Subjects: Automatic control
- Creators: Rivera, Daniel E
- Member of: Theses and Dissertations
Description
Control engineering offers a systematic and efficient approach to optimizing the effectiveness of individually tailored treatment and prevention policies, also known as adaptive or ``just-in-time'' behavioral interventions. These types of interventions represent promising strategies for addressing many significant public health concerns. This dissertation explores the development of decision algorithms for adaptive sequential behavioral interventions using dynamical systems modeling, control engineering principles and formal optimization methods. A novel gestational weight gain (GWG) intervention involving multiple intervention components and featuring a pre-defined, clinically relevant set of sequence rules serves as an excellent example of a sequential behavioral intervention; it is examined in detail in this research.
A comprehensive dynamical systems model for the GWG behavioral interventions is developed, which demonstrates how to integrate a mechanistic energy balance model with dynamical formulations of behavioral models, such as the Theory of Planned Behavior and self-regulation. Self-regulation is further improved with different advanced controller formulations. These model-based controller approaches enable the user to have significant flexibility in describing a participant's self-regulatory behavior through the tuning of controller adjustable parameters. The dynamic simulation model demonstrates proof of concept for how self-regulation and adaptive interventions influence GWG, how intra-individual and inter-individual variability play a critical role in determining intervention outcomes, and the evaluation of decision rules.
Furthermore, a novel intervention decision paradigm using Hybrid Model Predictive Control framework is developed to generate sequential decision policies in the closed-loop. Clinical considerations are systematically taken into account through a user-specified dosage sequence table corresponding to the sequence rules, constraints enforcing the adjustment of one input at a time, and a switching time strategy accounting for the difference in frequency between intervention decision points and sampling intervals. Simulation studies illustrate the potential usefulness of the intervention framework.
The final part of the dissertation presents a model scheduling strategy relying on gain-scheduling to address nonlinearities in the model, and a cascade filter design for dual-rate control system is introduced to address scenarios with variable sampling rates. These extensions are important for addressing real-life scenarios in the GWG intervention.
A comprehensive dynamical systems model for the GWG behavioral interventions is developed, which demonstrates how to integrate a mechanistic energy balance model with dynamical formulations of behavioral models, such as the Theory of Planned Behavior and self-regulation. Self-regulation is further improved with different advanced controller formulations. These model-based controller approaches enable the user to have significant flexibility in describing a participant's self-regulatory behavior through the tuning of controller adjustable parameters. The dynamic simulation model demonstrates proof of concept for how self-regulation and adaptive interventions influence GWG, how intra-individual and inter-individual variability play a critical role in determining intervention outcomes, and the evaluation of decision rules.
Furthermore, a novel intervention decision paradigm using Hybrid Model Predictive Control framework is developed to generate sequential decision policies in the closed-loop. Clinical considerations are systematically taken into account through a user-specified dosage sequence table corresponding to the sequence rules, constraints enforcing the adjustment of one input at a time, and a switching time strategy accounting for the difference in frequency between intervention decision points and sampling intervals. Simulation studies illustrate the potential usefulness of the intervention framework.
The final part of the dissertation presents a model scheduling strategy relying on gain-scheduling to address nonlinearities in the model, and a cascade filter design for dual-rate control system is introduced to address scenarios with variable sampling rates. These extensions are important for addressing real-life scenarios in the GWG intervention.
ContributorsDong, Yuwen (Author) / Rivera, Daniel E (Thesis advisor) / Dai, Lenore (Committee member) / Forzani, Erica (Committee member) / Rege, Kaushal (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2014
Description
Behavioral health problems such as physical inactivity are among the main causes of mortality around the world. Mobile and wireless health (mHealth) interventions offer the opportunity for applying control engineering concepts in behavioral change settings. Social Cognitive Theory (SCT) is among the most influential theories of health behavior and has been used as the conceptual basis of many behavioral interventions. This dissertation examines adaptive behavioral interventions for physical inactivity problems based on SCT using system identification and control engineering principles. First, a dynamical model of SCT using fluid analogies is developed. The model is used throughout the dissertation to evaluate system identification approaches and to develop control strategies based on Hybrid Model Predictive Control (HMPC). An initial system identification informative experiment is designed to obtain basic insights about the system. Based on the informative experimental results, a second optimized experiment is developed as the solution of a formal constrained optimization problem. The concept of Identification Test Monitoring (ITM) is developed for determining experimental duration and adjustments to the input signals in real time. ITM relies on deterministic signals, such as multisines, and uncertainty regions resulting from frequency domain transfer function estimation that is performed during experimental execution. ITM is motivated by practical considerations in behavioral interventions; however, a generalized approach is presented for broad-based multivariable application settings such as process control. Stopping criteria for the experimental test utilizing ITM are developed using both open-loop and robust control considerations.
A closed-loop intensively adaptive intervention for physical activity is proposed relying on a controller formulation based on HMPC. The discrete and logical features of HMPC naturally address the categorical nature of the intervention components that include behavioral goals and reward points. The intervention incorporates online controller reconfiguration to manage the transition between the behavioral initiation and maintenance training stages. Simulation results are presented to illustrate the performance of the system using a model for a hypothetical participant under realistic conditions that include uncertainty. The contributions of this dissertation can ultimately impact novel applications of cyberphysical system in medical applications.
A closed-loop intensively adaptive intervention for physical activity is proposed relying on a controller formulation based on HMPC. The discrete and logical features of HMPC naturally address the categorical nature of the intervention components that include behavioral goals and reward points. The intervention incorporates online controller reconfiguration to manage the transition between the behavioral initiation and maintenance training stages. Simulation results are presented to illustrate the performance of the system using a model for a hypothetical participant under realistic conditions that include uncertainty. The contributions of this dissertation can ultimately impact novel applications of cyberphysical system in medical applications.
ContributorsMartín Moreno, César Antonio (Author) / Rivera, Daniel E (Thesis advisor) / Hekler, Eric B. (Committee member) / Peet, Matthew M (Committee member) / Tsakalis, Konstantinos S (Committee member) / Arizona State University (Publisher)
Created2016